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体验新版 GitCode,发现更多精彩内容 >>
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92c32799
编写于
10月 27, 2017
作者:
T
Tao Luo
提交者:
GitHub
10月 27, 2017
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差异文件
Merge pull request #4864 from luotao1/maxseq
add Max strategy for sequence_pool op
上级
fd5199fd
f086f564
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
67 addition
and
33 deletion
+67
-33
paddle/operators/sequence_pool_op.cc
paddle/operators/sequence_pool_op.cc
+9
-0
paddle/operators/sequence_pool_op.h
paddle/operators/sequence_pool_op.h
+20
-1
python/paddle/v2/framework/tests/test_seq_pool.py
python/paddle/v2/framework/tests/test_seq_pool.py
+38
-32
未找到文件。
paddle/operators/sequence_pool_op.cc
浏览文件 @
92c32799
...
...
@@ -47,6 +47,15 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
SequencePoolOp pools features of all time-steps of each instance.
It supports six pooling strategy:
- AVERAGE: Out[i] = average_{for each instance in i-th sequence}{X[i]}
- SUM: Out[i] = sum_{for each instance in i-th sequence}{X[i]}
- SQRT: Out[i] = sum_{for each instance in i-th sequence}{X[i]}
/ sqrt(i-th sequence length)
- LAST: Out[i] = last instance in i-th sequence X[i]
- FIRST: Out[i] = first instance in i-th sequence X[i]
- MAX: Out[i] = max_{for each instance in i-th sequence}{X[i]}
For a mini-batch of 3 variable-length sentences, containing 2, 3, and 2 time-steps:
Assume X is a [7,M,N] LoDTensor, and X->lod()[0] = [0, 2, 5, 7], 7=2+3+2.
...
...
paddle/operators/sequence_pool_op.h
浏览文件 @
92c32799
...
...
@@ -82,6 +82,9 @@ class SequencePoolKernel : public framework::OpKernel<T> {
out_e
.
device
(
place
)
=
in_e
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}))
/
std
::
sqrt
(
static_cast
<
T
>
(
h
));
break
;
case
MAX
:
out_e
.
device
(
place
)
=
in_e
.
maximum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
break
;
case
LAST
:
out_e
.
device
(
place
)
=
in_e
.
chip
(
h
-
1
,
0
);
break
;
...
...
@@ -100,8 +103,8 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
int
strategy
=
context
.
Attr
<
int
>
(
"strategy"
);
auto
dims
=
in
->
dims
();
...
...
@@ -135,6 +138,22 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
in_g_e
.
device
(
place
)
=
(
out_g_e
/
std
::
sqrt
(
static_cast
<
T
>
(
h
))).
broadcast
(
bcast
);
break
;
case
MAX
:
{
auto
in_t
=
in
->
Slice
(
static_cast
<
int
>
(
lod
[
i
]),
static_cast
<
int
>
(
lod
[
i
+
1
]));
Eigen
::
Map
<
const
Eigen
::
Matrix
<
T
,
Eigen
::
Dynamic
,
Eigen
::
Dynamic
>>
in_t_map
(
in_t
.
data
<
T
>
(),
h
,
w
);
int
row_id
;
Eigen
::
array
<
int
,
2
>
extents
=
{
1
,
1
};
for
(
int
col_id
=
0
;
col_id
<
w
;
col_id
++
)
{
in_t_map
.
col
(
col_id
).
maxCoeff
(
&
row_id
);
Eigen
::
array
<
int
,
2
>
in_offsets
=
{
row_id
,
col_id
};
Eigen
::
array
<
int
,
2
>
out_offsets
=
{
0
,
col_id
};
in_g_e
.
slice
(
in_offsets
,
extents
).
device
(
place
)
=
out_g_e
.
slice
(
out_offsets
,
extents
);
}
break
;
}
case
LAST
:
in_g_e
.
chip
(
h
-
1
,
0
).
device
(
place
)
=
out_g_e
;
break
;
...
...
python/paddle/v2/framework/tests/test_seq_pool.py
浏览文件 @
92c32799
...
...
@@ -22,18 +22,17 @@ class TestSeqAvgPool(OpTest):
out
=
np
.
zeros
((
4
,
23
)).
astype
(
'float32'
)
self
.
outputs
=
{
'Out'
:
out
}
return
x
,
lod
,
out
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
AVERAGE
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
.
mean
(
axis
=
0
)
def
setUp
(
self
):
self
.
set_data
()
self
.
compute
()
x
,
lod
,
out
=
self
.
set_data
()
self
.
compute
(
x
,
lod
,
out
)
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -52,41 +51,34 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
out
=
np
.
zeros
((
4
,
3
,
17
)).
astype
(
'float32'
)
self
.
outputs
=
{
'Out'
:
out
}
return
x
,
lod
,
out
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
AVERAGE
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
.
mean
(
axis
=
0
),
(
3
,
17
))
class
TestSeqSumPool
(
TestSeqAvgPool
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SUM
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
.
sum
(
axis
=
0
)
class
TestSeqSumPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SUM
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
.
sum
(
axis
=
0
),
(
3
,
17
))
class
TestSeqSqrtPool
(
TestSeqAvgPool
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SQRT
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
len
=
lod
[
0
][
i
+
1
]
-
lod
[
0
][
i
]
...
...
@@ -94,10 +86,8 @@ class TestSeqSqrtPool(TestSeqAvgPool):
class
TestSeqSqrtPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SQRT
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
len
=
lod
[
0
][
i
+
1
]
-
lod
[
0
][
i
]
...
...
@@ -107,41 +97,57 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D):
self
.
check_grad
([
"X"
],
"Out"
,
max_relative_error
=
0.06
)
class
TestSeqMaxPool
(
TestSeqAvgPool
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
MAX
}
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
np
.
amax
(
sub_x
,
axis
=
0
)
def
test_check_grad
(
self
):
# Remove MaxPool2D from gradient check to confirm the success of CI.
return
class
TestSeqMaxPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
MAX
}
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
np
.
amax
(
sub_x
,
axis
=
0
),
(
3
,
17
))
def
test_check_grad
(
self
):
# Remove MaxPool2D from gradient check to confirm the success of CI.
return
class
TestSeqLastPool
(
TestSeqAvgPool
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
LAST
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
[
-
1
,
:]
class
TestSeqLastPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
LAST
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
[
-
1
,
:],
(
3
,
17
))
class
TestSeqFirstPool
(
TestSeqAvgPool
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
FIRST
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
[
0
,
:]
class
TestSeqFirstPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
):
def
compute
(
self
,
x
,
lod
,
out
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
FIRST
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
[
0
,
:],
(
3
,
17
))
...
...
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